Machine Learning in Medical Diagnosis: From Image Analysis to EHRs
Main Article Content
Keywords
machine learning, medical diagnosis, electronic health records (EHR), deep learning models
Abstract
This article researched the widespread application of machine learning in medical diagnosis in depth, hoping to solve many of the current challenges in the medical field by improving the accuracy and efficiency of diagnosis. This paper expounds the important role of machine learning in disease prediction and patient risk stratification from three aspects: medical image analysis, electronic health record (EHR) processing and time series data analysis. Through algorithms such as supervised learning, unsupervised learning, and reinforcement learning, machine learning can extract valuable information from massive medical data to help doctors make more accurate diagnostic decisions. In the paper, the application of convolutional neural network (CNN), bidirectional encoder representation (BERT) and long short-term memory network (LSTM) in medical diagnosis is studied. Specifically, CNN performed well in medical image analysis and was able to quickly identify diseased areas; BERT extracts key information from unstructured text through natural language processing to support disease prediction and drug recommendations. LSTM specializes in processing time series data and can predict a patient’s disease risk. In addition, the successful cases of these models in data analysis such as pneumonia detection and heart disease prediction are also introduced. This shows their great potential in the field of medicine. Finally, the paper discusses the challenges of data privacy, model interpretability and algorithm optimization, and looks forward to future research directions such as federated learning, multimodal data fusion and real-time diagnosis, providing theoretical support and practical guidance for the intelligent transformation of healthcare industry.
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